Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks

In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in the aspects of robustness, fault tolerance and scalability.Comment: To appear in Journal of Computer and System Science

Unsupervised Domain Adaptive Graph Convolutional Networks

Graph convolutional networks (GCNs) have achieved impressive success in many graph related analytics tasks. However, most GCNs only work in a single domain (graph) incapable of transferring knowledge from/to other domains (graphs), due to the challenges in both graph representation learning and domain adaptation over graph structures. In this paper, we present a novel approach, unsupervised domain adaptive graph convolutional networks (UDA-GCN), for domain adaptation learning for graphs. To enable effective graph representation learning, we first develop a dual graph convolutional network component, which jointly exploits local and global consistency for feature aggregation. An attention mechanism is further used to produce a unified representation for each node in different graphs. To facilitate knowledge transfer between graphs, we propose a domain adaptive learning module to optimize three different loss functions, namely source classifier loss, domain classifier loss, and target classifier loss as a whole, thus our model can differentiate class labels in the source domain, samples from different domains, the class labels from the target domain, respectively. Experimental results on real-world datasets in the node classification task validate the performance of our method, compared to state-of-the-art graph neural network algorithms

Joint Adversarial Domain Adaptation

Domain adaptation aims to transfer the enriched label knowledge from large amounts of source data to unlabeled target data. It has raised significant interest in multimedia analysis. Existing researches mainly focus on learning domain-wise transferable representations via statistical moment matching or adversarial adaptation techniques, while ignoring the class-wise mismatch across domains, resulting in inaccurate distribution alignment. To address this issue, we propose a Joint Adversarial Domain Adaptation (JADA) approach to simultaneously align domain-wise and class-wise distributions across source and target in a unified adversarial learning process. Specifically, JADA attempts to solve two complementary minimax problems jointly. The feature generator aims to not only fool the well-trained domain discriminator to learn domain-invariant features, but also minimize the disagreement between two distinct task-specific classifiers' predictions to synthesize target features near the support of source class-wisely. As a result, the learned transferable features will be equipped with more discriminative structures, and effectively avoid mode collapse. Additionally, JADA enables an efficient end-to-end training manner via a simple back-propagation scheme. Extensive experiments on several real-world cross-domain benchmarks, including VisDA-2017, ImageCLEF, Office-31 and digits, verify that JADA can gain remarkable improvements over other state-of-the-art deep domain adaptation approaches

Atypical radio pulsations from magnetar SGR 1935+2154

Magnetars are neutron stars with extremely strong magnetic fields, frequently powering high-energy activity in X-rays. Pulsed radio emission following some X-ray outbursts have been detected, albeit its physical origin is unclear. It has long been speculated that the origin of magnetars' radio signals is different from those from canonical pulsars, although convincing evidence is still lacking. Five months after magnetar SGR 1935+2154's X-ray outburst and its associated Fast Radio Burst (FRB) 20200428, a radio pulsar phase was discovered. Here we report the discovery of X-ray spectral hardening associated with the emergence of periodic radio pulsations from SGR 1935+2154 and a detailed analysis of the properties of the radio pulses. The complex radio pulse morphology, which contains both narrow-band emission and frequency drifts, has not been seen before in other magnetars, but is similar to those of repeating FRBs - even though the luminosities are many orders of magnitude different. The observations suggest that radio emission originates from the outer magnetosphere of the magnetar, and the surface heating due to the bombardment of inward-going particles from the radio emission region is responsible for the observed X-ray spectral hardening.Comment: 47 pages, 11 figure

Enhanced Enzymatic Hydrolysis of Poplar after Combined Dilute NaOH and Fenton Pretreatment

Five types of pretreatment processes were investigated to confirm the enhancement of the enzymatic hydrolysis of poplar. These processes included a hot water pretreatment, a calcium oxide pretreatment, NaOH extraction at low temperature, a Fenton reaction, and a combined dilute NaOH and Fenton pretreatment. The combined dilute NaOH and Fenton pretreatment was found to be the most effective pretreatment process. After enzymatic hydrolysis for 72 h, 74% of the cellulose recovery yield was obtained when the poplar substrates were pretreated with 2% NaOH at 75 °C for 3 h, followed by 20 mmol/g of H2O2 (30%) and 0.2 mmol/g of FeSO4·7H2O for a Fenton reaction period of 12 h. The cellulose recovery yield was approximately five-fold greater than that of the untreated sample directly processed by enzymatic hydrolysis. Furthermore, microscopic observations of changes in the surface structure of the pretreated residue were correlated with the enhancement of the enzymatic hydrolysis of cellulose. In conclusion, the combined dilute NaOH and Fenton pretreatment shows high potential for future application

m⁶A Methyltransferase KIAA1429 Regulates the Cisplatin Sensitivity of Gastric Cancer Cells via Stabilizing FOXM1 mRNA

Although cisplatin is frequently used to treat gastric cancer, the resistance is the main obstacle for effective treatment. mRNA modification, N6-methyladenosine (m6A), is involved in the tumorigenesis of many types of cancer. As one of the largest m6A methyltransferase complex components, KIAA1429 bridges the catalytic m6A methyltransferase components, such as METTL3. In gastric cancer, KIAA1429 was reported to promote cell proliferation. However, whether KIAA1429 is involved in the resistance of gastric cancer to cisplatin remains unclear. Here, we generated cisplatin resistant gastric cancer cell lines, and compared the m6A content between resistant cells and wild type cells. The m6A content as well as KIAA1429 expression are higher in resistant cells. Interestingly, the expression of KIAA1429 was significantly increased after cisplatin treatment. We then used shRNA to knockdown KIAA1429 and found that resistant cells responded more to cisplatin treatment after KIAA1429 depletion, while overexpression of KIAA1429 decreased the sensitivity. Moreover, we identified a putative p65 binding site on the promoter area of KIAA1429 and ChIP assay confirmed the binding. p65 depletion decreased the expression of KIAA1429. YTHDF1 is the most abundant m6A “reader” that interacts with m6A modified mRNA. Mechanistically, YTHDF1 was recruited to the 3′-untranslated Region (3′-UTR) of transcriptional factor, FOXM1 by KIAA1429 and stabilized FOXM1 mRNA. More importantly, KIAA1429 knockdown increased the sensitivity of resistant cells to cisplatin in vivo. In conclusion, our results demonstrated that KIAA1429 facilitated cisplatin resistance by stabilizing FOXM1 mRNA in gastric cancer cells

Palladium-Catalyzed [3 + 2]-C–C/N–C Bond-Forming Annulation

The synthesis of bi- and tricyclic structures incorporating pyrrolidone rings is disclosed, starting from resonance-stabilized acetamides and cyclic α,β-unsaturated-γ-oxycarbonyl derivatives. This process involves an intermolecular Tsuji–Trost allylation/intramolecular nitrogen 1,4-addition sequence. Crucial for the success of this bis-nucleophile/bis-electrophile [3 + 2] annulation is its well-defined step chronology in combination with the total chemoselectivity of the former step. When the newly formed annulation product carries a properly located <i>o</i>-haloaryl moiety at the nitrogen substituent, a further intramolecular keto α-arylation can join the cascade, thereby forming two new cycles and three new bonds in the same synthetic operation

Additional file 2: Table S2. of Neuron navigator 2 overexpression indicates poor prognosis of colorectal cancer and promotes invasion through the SSH1L/cofilin-1 pathway

The intensity of staining for NAV2 in 138 paired CRC cancer and normal mucosa samples (cohort 1) (DOC 28 kb)